Spotify_report

Spotify Data

variable	description
track_id	Song unique ID
track_name	Song Name
track_artist	Song Artist
track_popularity	Song Popularity (0-100) where higher is better
track_album_id	Album unique ID
track_album_name	Song album name
track_album_release_date	Date when album released
playlist_name	Name of playlist
playlist_id	Playlist ID
playlist_genre	Playlist genre
playlist_subgenre	Playlist subgenre
danceability	Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
energy	Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
key	The estimated overall key of the track. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.
loudness	The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.
mode	Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
speechiness	Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
acousticness	A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
instrumentalness	Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
liveness	Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
valence	A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).
tempo	The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
duration_ms	Duration of song in milliseconds

first initiate and download packages

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
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##     date, intersect, setdiff, union

library(GGally)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(janitor)

## 
## Attaching package: 'janitor'

## The following objects are masked from 'package:stats':
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##     chisq.test, fisher.test

library(here)

## here() starts at /home/rstudio/Documents/GroupAssigments#1

library(corrplot)

## corrplot 0.92 loaded

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
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##     last_plot

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##     layout

library(scales)
library(ggrepel)
library(reactable)
here::i_am("Spotify_Dataset_EDA.Rproj")

## here() starts at /home/rstudio/Documents/GroupAssigments#1

second we load the dataset

SpotifyData <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv')

## Rows: 32833 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): track_id, track_name, track_artist, track_album_id, track_album_na...
## dbl (13): track_popularity, danceability, energy, key, loudness, mode, speec...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Interactive tables

using the glimpse() : this function allows the features to be displayed vertically while the data can be viewed horizontally

dplyr::glimpse(SpotifyData)

## Rows: 32,833
## Columns: 23
## $ track_id                 <chr> "6f807x0ima9a1j3VPbc7VN", "0r7CVbZTWZgbTCYdfa…
## $ track_name               <chr> "I Don't Care (with Justin Bieber) - Loud Lux…
## $ track_artist             <chr> "Ed Sheeran", "Maroon 5", "Zara Larsson", "Th…
## $ track_popularity         <dbl> 66, 67, 70, 60, 69, 67, 62, 69, 68, 67, 58, 6…
## $ track_album_id           <chr> "2oCs0DGTsRO98Gh5ZSl2Cx", "63rPSO264uRjW1X5E6…
## $ track_album_name         <chr> "I Don't Care (with Justin Bieber) [Loud Luxu…
## $ track_album_release_date <chr> "2019-06-14", "2019-12-13", "2019-07-05", "20…
## $ playlist_name            <chr> "Pop Remix", "Pop Remix", "Pop Remix", "Pop R…
## $ playlist_id              <chr> "37i9dQZF1DXcZDD7cfEKhW", "37i9dQZF1DXcZDD7cf…
## $ playlist_genre           <chr> "pop", "pop", "pop", "pop", "pop", "pop", "po…
## $ playlist_subgenre        <chr> "dance pop", "dance pop", "dance pop", "dance…
## $ danceability             <dbl> 0.748, 0.726, 0.675, 0.718, 0.650, 0.675, 0.4…
## $ energy                   <dbl> 0.916, 0.815, 0.931, 0.930, 0.833, 0.919, 0.8…
## $ key                      <dbl> 6, 11, 1, 7, 1, 8, 5, 4, 8, 2, 6, 8, 1, 5, 5,…
## $ loudness                 <dbl> -2.634, -4.969, -3.432, -3.778, -4.672, -5.38…
## $ mode                     <dbl> 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, …
## $ speechiness              <dbl> 0.0583, 0.0373, 0.0742, 0.1020, 0.0359, 0.127…
## $ acousticness             <dbl> 0.10200, 0.07240, 0.07940, 0.02870, 0.08030, …
## $ instrumentalness         <dbl> 0.00e+00, 4.21e-03, 2.33e-05, 9.43e-06, 0.00e…
## $ liveness                 <dbl> 0.0653, 0.3570, 0.1100, 0.2040, 0.0833, 0.143…
## $ valence                  <dbl> 0.518, 0.693, 0.613, 0.277, 0.725, 0.585, 0.1…
## $ tempo                    <dbl> 122.036, 99.972, 124.008, 121.956, 123.976, 1…
## $ duration_ms              <dbl> 194754, 162600, 176616, 169093, 189052, 16304…

a summary of the dataset

summary(SpotifyData)

##    track_id          track_name        track_artist       track_popularity
##  Length:32833       Length:32833       Length:32833       Min.   :  0.00  
##  Class :character   Class :character   Class :character   1st Qu.: 24.00  
##  Mode  :character   Mode  :character   Mode  :character   Median : 45.00  
##                                                           Mean   : 42.48  
##                                                           3rd Qu.: 62.00  
##                                                           Max.   :100.00  
##  track_album_id     track_album_name   track_album_release_date
##  Length:32833       Length:32833       Length:32833            
##  Class :character   Class :character   Class :character        
##  Mode  :character   Mode  :character   Mode  :character        
##                                                                
##                                                                
##                                                                
##  playlist_name      playlist_id        playlist_genre     playlist_subgenre 
##  Length:32833       Length:32833       Length:32833       Length:32833      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##   danceability        energy              key            loudness      
##  Min.   :0.0000   Min.   :0.000175   Min.   : 0.000   Min.   :-46.448  
##  1st Qu.:0.5630   1st Qu.:0.581000   1st Qu.: 2.000   1st Qu.: -8.171  
##  Median :0.6720   Median :0.721000   Median : 6.000   Median : -6.166  
##  Mean   :0.6548   Mean   :0.698619   Mean   : 5.374   Mean   : -6.720  
##  3rd Qu.:0.7610   3rd Qu.:0.840000   3rd Qu.: 9.000   3rd Qu.: -4.645  
##  Max.   :0.9830   Max.   :1.000000   Max.   :11.000   Max.   :  1.275  
##       mode         speechiness      acousticness    instrumentalness   
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000000  
##  1st Qu.:0.0000   1st Qu.:0.0410   1st Qu.:0.0151   1st Qu.:0.0000000  
##  Median :1.0000   Median :0.0625   Median :0.0804   Median :0.0000161  
##  Mean   :0.5657   Mean   :0.1071   Mean   :0.1753   Mean   :0.0847472  
##  3rd Qu.:1.0000   3rd Qu.:0.1320   3rd Qu.:0.2550   3rd Qu.:0.0048300  
##  Max.   :1.0000   Max.   :0.9180   Max.   :0.9940   Max.   :0.9940000  
##     liveness         valence           tempo         duration_ms    
##  Min.   :0.0000   Min.   :0.0000   Min.   :  0.00   Min.   :  4000  
##  1st Qu.:0.0927   1st Qu.:0.3310   1st Qu.: 99.96   1st Qu.:187819  
##  Median :0.1270   Median :0.5120   Median :121.98   Median :216000  
##  Mean   :0.1902   Mean   :0.5106   Mean   :120.88   Mean   :225800  
##  3rd Qu.:0.2480   3rd Qu.:0.6930   3rd Qu.:133.92   3rd Qu.:253585  
##  Max.   :0.9960   Max.   :0.9910   Max.   :239.44   Max.   :517810

to get a on overview of the dataset we used the corrgram() function to display the correlation of all the numeric feature

featuresCorr <- dplyr::select(SpotifyData , track_popularity, danceability, energy, loudness, speechiness, acousticness, instrumentalness, liveness, valence, tempo ,key)
corrgram::corrgram(cor(featuresCorr),order = TRUE, upper.panel =  NULL)

TopMonth=SpotifyData %>%
  select(-c(("track_id"),("track_album_id"),("playlist_name"),("playlist_id"),("duration_ms")))

we wanted to see if release month is effected by genre.

TopMonth=SpotifyData %>%
  select(-c(("track_id"),("track_album_id"),("playlist_name"),("playlist_id"),("duration_ms")))

TopMonth$track_album_release_date=as.Date(TopMonth$track_album_release_date)

TopMonth=TopMonth %>%
  mutate(month=month(TopMonth$track_album_release_date),year=year(TopMonth$track_album_release_date)) %>%
  select(-c("track_album_release_date"))

TopMonth =TopMonth%>%
  mutate(period = case_when(
      month == 1  ~ 'january'
    , month == 2  ~ 'february'
    , month == 3  ~ 'march'
    , month == 4  ~ 'april'
    , month == 5  ~ 'may'
    , month == 6  ~ 'june'
    , month == 7  ~ 'july'
    , month == 8  ~ 'august'
    , month == 9  ~ 'september'
    , month == 10  ~ 'october'
    , month == 11  ~ 'november'
    , month == 12  ~ 'december'))

TopMonth= TopMonth %>%
  group_by(period) %>%
  count(playlist_genre)

TopMonth = na.omit(TopMonth)

TopMonth$period = factor(TopMonth$period, levels = c('january', 'february', 'march', 'april', 'may', 'june' ,'july' ,'august', 'september', 'october', 'november' ,'december'))

library("ggplot2")
ggplot(TopMonth,aes(x=period,y=n, group = playlist_genre))+
  geom_point(aes(colour=playlist_genre))+
  geom_line(aes(colour=playlist_genre))+
  xlab("Month")+ylab("Number of songs")+
  ggtitle("Number of songs released during each month") 

From the above line chart we cannot conclude there is any correctional between genre and release date.

Are tracks with speech/words more popular than instrumental? to test this we got the median popularity for with a high speechiness and songs with high instrumentalness

SpotifyData %>% filter(speechiness > .80)  %>% pull(track_popularity) %>% median()

## [1] 54.5

SpotifyData %>% filter(instrumentalness  > .80)  %>% pull(track_popularity) %>% median()

## [1] 36

Distribution of loudness across genres

ggplot(data = SpotifyData, mapping = aes(x = playlist_genre, y=loudness, fill=playlist_genre, alpha=0.05)) +
  geom_boxplot()+
  theme_bw()+
  theme(legend.position = "none")+
  labs(x = "Playlist Genres",
       y = "loudness",
       title = "Distribution of loudness by using boxpplot")

which genre is the most popular?

ggplot(SpotifyData, aes(x = track_popularity, ..density.., color = playlist_genre)) + 
  geom_freqpoly() +
  coord_cartesian(ylim = c(0, 500)) + 
  coord_cartesian(xlim = c(20, 120)) + 
  NULL

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# rock genre has very interesting popularity distribution (right-hand tail)
SpotifyData %>% 
  filter(playlist_genre == "rock") %>% 
  filter(track_popularity > 5) %>% 
  
  ggplot(aes(track_popularity)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# compared to pop genre

SpotifyData %>% 
  filter(playlist_genre == "pop") %>% 
  filter(track_popularity > 5) %>% 
  
  ggplot(aes(track_popularity)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

what key is played the most

ggplot(SpotifyData) + geom_bar(ggplot2::aes( key),width = 0.3) +
  labs(x = "keys")